NURS FPX 6111 Assessment 4 Program Effectiveness Presentation

Name

Capella University

NURS-FPX 6111 Assessment and Evaluation in Nursing Education

Prof. Name

Date

Greetings, esteemed audience. I am_____. In today’s discussion, we shall explore the efficacy of the BSN curriculum, focusing on the implementation of the “Healthcare Technology Management” (HTM) course.  

 Program Effectiveness Presentation

The core objective of this presentation is to shed light on the efficacy of the BSN program and to propose significant enhancements for seamlessly integrating the proposed course into the nursing curriculum. This assessment is crucial before the integration of any new course to ensure continual advancement. Evaluating program effectiveness aids in identifying strengths and areas for improvement. This evaluation ensures curriculum alignment with evolving healthcare standards. This process facilitates the enhancement of student outcomes, compliance with management criteria, and adaptation of the curriculum to emerging trends in nursing practice. This systematic assessment guarantees that the introduction of any new course is grounded in data-driven decision-making. This assessment raises an environment conducive to self-driven learning and receptiveness among aspiring nurses (Oermann et al., 2024).

Purpose 

The presentation will proceed as follows:

1. Exploring various philosophical approaches to evaluation and scrutinizing the evidence employed for clarification.
2. Introducing the steps of the program evaluation process and examining the interconnected limitations.
3. Outlining an evaluation framework/design for program assessment and delving into its associated constraints.
4. This section details how data analysis facilitates continuous program evaluation and addresses knowledge gaps and uncertainties that require additional information.

Evaluation of philosophical approaches

Evaluation of  the HTM course encompasses various philosophical approaches, including:

Benner’s Model

This model categorizes learners into novice, advanced beginners, competent, proficient, and expert. It evaluates the learner’s capacity to comprehend, acquire, and tackle issues based on their proficiency in the HTM. The model suggests that individuals starting as novices with little to no knowledge of the HTM progressively enhance their skills and practical understanding throughout the course, particularly in integrating technology with healthcare services (Murray et al., 2019). 

DIKW Theory

The Data, Information, Knowledge, and Wisdom (DIKW) theory involves gathering raw data, which is then synthesized to generate information. This information is analyzed alongside existing evidence to extract knowledge, enhancing understanding of the subject matter. In the HTM course, DIKW facilitates the provision of data, information, knowledge, and wisdom to students, intending to augment their understanding of technology integration within healthcare services (Peters et al., 2024).

Summative and Formative Assessments

Formative assessments gauge the learner’s proficiency in employing integrated healthcare with technological advancements during the study period. On the other hand, summative assessments evaluate the learner’s capacity to apply acquired knowledge after completing the HTM course. Both assessments are instrumental in assessing students’ aptitude to effectively apply the gained knowledge throughout the course (Arrogante et al., 2021).

Supporting Evidence For The Explanation

The DIKW Theory, Benner’s Model, and Formative and Summative assessments seek to gather, analyze, and extract knowledge concerning integrating Information Technology (IT) with healthcare services (Murray et al., 2019). These models aim to teach students about using simulation-based healthcare services, Artificial Intelligence (AI), and other technologies like automated IV pumps, EMR, and remote monitoring services. They aim to evaluate students’ learning progress during and after the HTM course (Peters et al., 2024). 

Process of Evaluating Program

 The HTM course evaluation process is structured as follows:

Evaluation

Data is gathered from HTM students via questionnaires and survey forms during this stage, ensuring anonymity. The collected data follows a structured approach with closed-ended questions, facilitating specific responses.

Analysis

Subsequently, the collected data is synthesized to extract precise perceptivities regarding the HTM course. This data furnishes details on the pertinence of healthcare education and the course’s efficacy in nurturing technological competencies for delivering efficient healthcare services (Oermann et al., 2024).

Strategizing

Insights from the analysis phase are utilized to assess the course’s effectiveness, specifically in cultivating the utilization of technology among HTM students to ensure the delivery of safe and high-quality patient care.

Execution

During the implementation stage, targeted adjustments are integrated into the course structure to address any deficiencies, aiming to enhance nurses’ proficiency across cognitive, affective, and psychomotor domains.

Assessment

Students were assessed through the distribution of questionnaires and survey forms to gauge the effectiveness of the changes implemented in the HTM course. The evaluation sought to determine whether the modifications added value to the course. The Likert Scale was employed as the evaluation instrument due to its high-reliability score of 94% (Jowsey et al., 2020).

Limitation of Steps in Process

The limitations of the process steps encompass the following:

• Insufficient data availability stemming from student non-participation.
• Errors during the collection of survey forms led to data mismanagement.
• Incorrect application of technology during data collection, resulting in the use of inappropriate question formats.
• Errors in data analysis due to the use of improper analytical methods.
• Subpar skills employed in data evaluation, including improper timing for conducting program assessments, such as formative and summative evaluations (Jowsey et al., 2020).

Model for Program Enhancement

The enhancing HTM program model incorporates the Plan-Do-Study-Act (PDSA) cycle. This renowned approach analyzes necessary changes within the program, particularly in the context of HTM coursework. The plan is designed to evaluate the alignment between the course’s learning objectives and actual outcomes, focusing on enhancing students’ understanding of technology integration in healthcare services (Mukwato, 2020). Additionally, the model evaluates the effectiveness of simulation-based learning and evidence-based practices in HTM education, ensuring students can proficiently apply cognitive, psychomotor, and affective skills to deliver safe, high-quality patient care (Mukwato, 2020). 

The collected data will undergo analysis to determine if HTM students have acquired, applied, and proficiently utilized technology integrated with healthcare services in patient scenarios. If discrepancies arise between the course’s learning objectives and actual outcomes, a corrective plan will be developed to address these gaps. Subsequently, the plan will be implemented, and three months later, a re-evaluation will be conducted to assess the effectiveness of gap bridging. Questionnaires will be used for data collection, and evaluation will be performed using the Likert Scale to evaluate students’ learning in simulation-based scenarios (Joyce et al., 2019).

Limitations of the PDSA cycle:

Limitations associated with the PDSA cycle comprise:

• Infrequent data collection
• Timing issues in data collection (Joyce et al., 2019)

Data Analysis for Continuous Program Enhancement

Data analysis informs ongoing improvements in the HTM education program. This analysis evaluates the ongoing examination’s efficacy, with frequent data collection ensuring the authenticity of program success assessment via the Likert Scale. Continuous data analysis throughout the program offers insights into its success status, facilitating timely adjustments if necessary (Rouleau et al., 2019). This ensures uninterrupted student education and continual effective learning of technology integrated with healthcare services. Furthermore, ongoing program analysis assesses its effectiveness in student learning and evaluates its ability to deliver healthcare services effectively through technology utilization (Rouleau et al., 2019).

Knowledge Gaps

Utilizing closed-ended questions instead of open-ended ones limits the richness of student responses. By restricting responses, questionnaires fail to capture comprehensive information, hindering thorough data evaluation. Consequently, student inquiries and issues can remain unaddressed, impeding the implementation of necessary course improvements (Spurlock et al., 2019).

Conclusion

In conclusion, ongoing evaluation and refinement of the HTM program are essential for ensuring its relevance and effectiveness. Despite challenges in data collection and timing, continuous improvement efforts are crucial. By leveraging evaluation insights, the program can better prepare students for success in integrating technology with healthcare services, thereby meeting the evolving needs of the healthcare industry.

References

Arrogante, O., Romero, G. M. G., Torre, E. M. L., García, L. C., & Polo, A. (2021). Comparing formative and summative simulation-based assessment in undergraduate nursing students: Nursing competency acquisition and clinical simulation satisfaction. BMC Nursing, 20(1). https://doi.org/10.1186/s12912-021-00614-2 

Jowsey, T., Foster, G., Ioelu, P. C., & Jacobs, S. (2020). Blended learning via distance in pre-registration nursing education: A scoping review. Nurse Education in Practice, 44, 102775. https://doi.org/10.1016/j.nepr.2020.102775 

Joyce, B. L., Harmon, M. J., Johnson, R. (Gina) H., Hicks, V., Schott, N. B., & Pilling, L. B. (2019). Using a quality improvement model to enhance community/public health nursing education. Public Health Nursing, 36(6), 847–855. https://doi.org/10.1111/phn.12656 

Mukwato, P. K. (2020). Implementing evidence based practice nursing using the PDSA model: Process, lessons and implications. International Journal of Africa Nursing Sciences, 14, 100261. https://doi.org/10.1016/j.ijans.2020.100261 

Murray, M., Sundin, D., & Cope, V. (2019). Benner’s model and Duchscher’s theory: Providing the framework for understanding new graduate nurses’ transition to practice. Nurse Education in Practice, 34(1), 199–203. https://doi.org/10.1016/j.nepr.2018.12.003

NURS FPX 6111 Assessment 4 Program Effectiveness Presentation

Oermann, M. H., Gaberson, K. B., & De, J. C. (2024). Evaluation and testing in nursing education (7th ed., p. 460). Springer Publishing Company. https://books.google.com.pk/books?hl=en&lr=&id=jPHbEAAAQBAJ&oi=fnd&pg=PP1&dq=education+program+evaluation+benefits,+nursing+education+&ots=_M1t3UoEYh&sig=jBaYgSi2maNxDorD27jxwNLm1VE&redir_esc=y#v=onepage&q=education%20program%20evaluation%20benefits%2C%20nursing%20education&f=false 

Peters, M. A., Jandrić, P., & Green, B. J. (2024). The DIKW model in the age of artificial intelligence. Postdigital Science and Education. https://doi.org/10.1007/s42438-024-00462-8 

Rouleau, G., Gagnon, M.-P., Côté, J., Gagnon, J. P., Hudson, E., Dubois, C.-A., & Picasso, J. B. (2019). Effects of e-learning in a continuing education context on nursing care: Systematic review of systematic qualitative, quantitative, and mixed-studies reviews. Journal of Medical Internet Research, 21(10), e15118. https://doi.org/10.2196/15118 

Spurlock, D. R., Patterson, B. J., & Colby, N. (2019). Gender differences and similarities in accelerated nursing education programs. Nursing Education Perspectives, 40(6), 343–351. https://doi.org/10.1097/01.nep.0000000000000508 

NURS FPX 6111 Assessment 3 Course Evaluation Template

Name

Capella University

NURS-FPX 6111 Assessment and Evaluation in Nursing Education

Prof. Name

Date

 Course Evaluation Template

In Healthcare Technology Management (HTM), instructors administer a concise survey to students after a course to assess their experiences. This assessment questionnaire aims to gather comprehensive feedback to improve the learning environment for future classes. Due to its proven reliability and validity, the Likert scale is utilized to evaluate HTM course outcomes. This rating tool consists of five levels of agreement, grading from strongly agree to strongly disagree, with additional categories for neutrality to ensure impartial feedback (Ephraim, 2020). 

Template for Standardized Course Evaluation

Various evaluation systems have been developed to provide a precise quantitative evaluation of opinions. The Likert scale allows the participant to rate their level of agreement or disagreement with the given information. This tool typically uses a five-point scale from strongly agree to disagree strongly, providing valuable insight into students’ understanding and perspectives on HTM (Ephraim, 2020).

Correlation Between Learning Objectives and Evaluation Chart

In the HTM course, “learning outcomes” refer to the anticipated levels of understanding, proficiency, and behavior that students are expected to demonstrate upon completing the program. The evaluation uses the Likert scale to gauge student responses regarding various learning outcomes. Student feedback is categorized into disagree, slightly agree, and strongly agree, allowing for the identification of proportions between those confident in their knowledge and those not. Insights into students’ readiness to utilize healthcare technology integration is obtained from their responses. Analyzing learners’ test results within this framework can effectively assess educational outcomes (Harerimana & Mtshali, 2020).

Strategies of Assessment 

The program evaluation will incorporate open-ended and closed-ended questions, ensuring students receive a comprehensive reflection of their learning experience. The essence of course assessment is to assess the alignment between program content, intended learning outcomes, and the instructor’s strategies for achieving those objectives (Harerimana & Mtshali, 2020).

Addressing  Suitable Cognitive, Psychomotor, and Affective Areas

Assumptions

The evaluation of the program is based on the following assumptions:

• The quality of education correlates directly with the effort instructors invest in their students.
• Standardization of course objectives or student outcomes is necessary to ensure the validity and utility of student assessments (Pizzuti et al., 2020).
• Throughout the curriculum, students will be assessed on their ability to utilize technology for illness prevention, diagnosis, and treatment across various cognitive, psychomotor, and affective domains.
• The degree to which students can apply framework learning, both with and without technology, to enhance the efficacy of the treatment provided (Pizzuti et al., 2020).

Format for Evaluating Learning Outcomes

The Likert scale has been utilized in this setting to explore the alignment between the program’s stated objectives and achieved outcomes. These findings have been leveraged to assess learner advancement and potential. Studies have utilized it to evaluate the congruence between the program’s intended outcomes and actual student attitudes, behaviors, and comprehension shifts. The data from the assessment were utilized to identify areas of instruction requiring enhancement to facilitate the most significant improvement in student performance (Huang et al., 2023).

Criteria Employed to Evaluate the Format

The assessment criteria for the format encompass comprehensiveness, user-friendliness, flexibility, validity, and reliability. It ensures a comprehensive evaluation of all pertinent learning objectives and program outcomes. This format facilitates a thorough assessment of the entire educational journey. The format is designed to be user-friendly, promoting ease of completion for students and encouraging high levels of participation and genuine feedback. Its flexibility allows for adaptation to various courses and learning environments in diverse educational settings. The evaluation format is engineered to gauge the course’s effectiveness across different contexts (Huang et al., 2023).

Ensuring Validity and Reliability in Course Evaluation Methods

The evaluation process, which incorporates both open-ended and closed-ended questionnaires, demonstrates strong validity and reliability. With a reliability rate of 94%, the Likert Scale yields widely recognized and trusted results, hence serving as the foundation for this assessment (Taira et al., 2021).

Strengths of the Likert Scale

• Ease of Use
• Answer Options with Measurable Quantities
• Respondents confidently complete questionnaires
• Responses can be efficiently coded
• A swift, dependable, and effective method for assessment and data collection (Jebb et al., 2021).

Weaknesses of the Likert Scale

• It can be challenging to address responses categorized as “neither agree nor disagree.”
• Individuals may tend to agree with all statements if they tend to approve.
• Respondents may not always provide completely honest responses.
• There is a possibility of incomplete surveys or assessments (Taira et al., 2021).

Executive Summary

The course evaluation for HTM utilized a comprehensive assessment template, incorporating the Likert scale for its reliability and validity. The evaluation aimed to gauge alignment with program objectives, student growth, and proficiency in healthcare IT. Learning outcomes were assessed across cognitive, psychomotor, and affective domains. These outcomes revealed advancements in critical thinking and technological proficiency. The evaluation process relied on assumptions. The assumptions were regarding the correlation between educational quality and instructor effort, standardized course objectives, and the application of technology in healthcare. The assessment criteria focused on comprehensiveness, user-friendliness, and flexibility, ensuring a thorough evaluation of program effectiveness. Despite the strengths of the Likert scale, challenges included addressing neutral responses and ensuring respondents provided honest feedback. The evaluation process offered valuable insights into program outcomes and areas for improvement. (Jebb et al., 2021).

References

Ephraim, N. (2020). Mentoring in nursing education: An essential element in the retention of new nurse faculty. Journal of Professional Nursing, 37(2), 306–319. https://doi.org/10.1016/j.profnurs.2020.12.001 

Harerimana, A., & Mtshali, N. G. (2020). Using exploratory and confirmatory factor analysis to understand the role of technology in nursing education. Nurse Education Today, 92, 104490. https://doi.org/10.1016/j.nedt.2020.104490 

Huang, H.-M., Huang, C.-Y., Lin, K.-C., Yu, C.-H., & Cheng, S.-F. (2023). Development and psychometric testing of the clinical reasoning scale among nursing students enrolled in three types of programs in Taiwan. Journal of Nursing Research, 31(2), e263. https://doi.org/10.1097/jnr.0000000000000547 

Jebb, A. T., Ng, V., & Tay, L. (2021). A review of key likert scale development advances: 1995–2019. Frontiers in Psychology, 12(1), 1–14. https://doi.org/10.3389/fpsyg.2021.637547 

NURS FPX 6111 Assessment 3 Course Evaluation Template

Pizzuti, A. G., Patel, K. H., McCreary, E. K., Heil, E., Bland, C. M., Chinaeke, E., Love, B. L., & Bookstaver, P. B. (2020). Healthcare practitioners’ views of social media as an educational resource. Plos One, 15(2), e0228372. https://doi.org/10.1371/journal.pone.0228372 

Taira, B. R., Kreger, V., Orue, A., & Diamond, L. C. (2021). A Pragmatic Assessment of Google Translate for Emergency Department Instructions. Journal of General Internal Medicine, 3361–3365. https://doi.org/10.1007/s11606-021-06666-z 

NURS FPX 6111 Assessment 2 Criteria and Rubric Development

Name

Capella University

NURS-FPX 6111 Assessment and Evaluation in Nursing Education

Prof. Name

Date

Criteria and Rubric Development

Establishing criteria and rubrics is essential to ensuring practical assessment and analysis of students’ knowledge. These tools offer students clear guidelines on what is expected of them, facilitating the evaluation of their learning abilities and capabilities (Authement & Dormire, 2020).

Description of Assessment 

The assessment will evaluate the accomplishment of learning goals in Health Technology Management (HTM) and assess students’ cognitive abilities to apply theoretical knowledge in practical contexts. The course objectives center on enhancing students’ capacity to implement technology effectively in healthcare settings  (Farahani et al., 2020). This implementation will ensure patient-centered care, fairness, and ethical practices. Moreover, it aims to foster students’ cognitive skills to integrate evidence-based approaches with technological advancements to address real-world challenges in healthcare management  (Farahani et al., 2020).

Specific Learning Outcomes

The students will be evaluated on their proficiency in utilizing technology for tasks such as updating patient information, e-monitoring, diagnosis, providing patient-centered treatment, and assessing drug-drug interactions using digital automated systems (Pottle, 2019). Assessment will focus on their ability to effectively apply evidence-based learning, critical thinking, and practical experience gained through simulation-based learning to deliver patient-centered care technologically. Furthermore, the assessment will analyze students’ capability to navigate complex scenarios calmly and accurately, as evaluated through simulation-based learning and theoretical examinations (Pottle, 2019).

Assessment Serving the Needs of The Learners

The assessment serves the learners’ needs by focusing on the integration of technology within healthcare management. Students will gain proficiency in utilizing technological advancements to enhance patient care, including Automated IV Pumps, EHR systems, telemedicine, Electronic Prescription systems, and Central Command Center (CCC) (Ngiam & Khor, 2019). This will enable them to leverage technology to mitigate Medication Errors (MEs). The reduced errors will reduce mortality, morbidity rates, and healthcare disparities associated with such errors (Ngiam & Khor, 2019).

Steps in Assembling and Administrating Test

A sequential list of steps is essential for assembling and administering tests tailored to specific learning outcomes in the HTM course. These steps ensure adherence to proper procedures in evaluating students’ proficiency in achieving the desired learning outcomes (De Lima et al., 2022).

Assembling The Test

Assembling the test for the HTM course involves carefully selecting course contents that form the foundation of students’ learning. These contents serve as the basis for the educational journey throughout the course. Intricate questions are formulated following content selection to effectively assess students’ cognitive abilities (Ali et al., 2020). A board of professors specializing in HTM expertly inspects these questions, ensuring they are academically challenging. Subsequently, the questions are arranged in a progressive order, starting from easier to more challenging ones. This strategic sequencing aims to motivate students while pushing their cognitive and critical thinking skills to their limits (Ali et al., 2020).

Administrating of the Test

The test will be administered to students after providing thorough guidance on the guidelines, criteria, and rubrics. They will receive detailed instructions on the requirements and rubric necessary for achieving high grades in their assessment (Shah et al., 2022). The following steps will be followed during the test administration 

1. Guidelines: 

The instructor will brief students on the rules, regulations, and instructions for effectively completing the test. Students will be encouraged to apply their cognitive thinking, theoretical knowledge, and simulation-based learning to solve test problems.

1. Learning Outcomes:

Students will be evaluated based on their application of simulation-based learning, theoretical knowledge, and cognitive thinking in the test.

1. Assessment:

Students’ ability to utilize technology in simulation-based learning to deliver effective healthcare services will be assessed, reflecting psychomotor solid, cognitive, and affective learning abilities (Shah et al., 2022).

Knowledge Gaps

  Potential knowledge gaps include professors’ ability to collaborate effectively in designing simulation-based learning assessments that integrate healthcare with technology usage. Additionally, the absence of a standardized testing format for the assessment could pose challenges in assembling and administering tests (De Lima et al., 2022).

Multiple Domains Learning

Students underwent assessment across cognitive, psychomotor, and affective domains within the realm of  HTM. 

Cognitive

Students’ cognitive abilities were evaluated through exposure to diverse scenarios that challenged their critical thinking skills. Their cognitive knowledge was assessed through simulation-based scenarios and theoretical and practical examinations. These assessments enhanced students’ analytical reasoning, and these exams improved their capacity to utilize technology effectively within healthcare settings (Miller et al., 2020). 

Psychomotor

The psychomotor skills of students were enhanced through practical examinations and clinical-based simulations. These skills enabled healthcare professionals to proficiently utilize technology in delivering fundamental healthcare services such as blood pressure monitoring, EHR usage, Automated IV pump operation, catheter insertions, and administering IV infusions and injections. These skills were assessed through theoretical, practical, and simulation-based examinations (Martinengo et al., 2019).

Affective

In this aspect, the assessment evaluated the ability of healthcare professionals to apply evidence-based learning in their professional endeavors. They affirmed their actions with theoretical knowledge derived from research articles (Martins et al., 2020).

Knowledge Gaps

The absence of simulation-based technology, such as patient simulators, EHR systems, and Automated IV pumps, will hinder the comprehensive development of students’ cognitive, psychomotor, and affective domains. Similarly, professors’ lack of proficiency and expertise in utilizing technology can impede students’ effective learning experiences (Martinengo et al., 2019).

Performance-Level Criteria

Communicating Grading Expectations

The instructor should effectively communicate grading expectations to learners through clear, detailed, and well-articulated guidelines. Utilizing open-line communication, each aspect of the grading criteria should be thoroughly explained to ensure students understand the distinctions between non-performance, basic, proficient, and distinguished criteria. Clarifying how these criteria differ from each other and guiding students in achieving distinguished standards is essential (Leaver et al., 2021).

Reliability and Validity of Assessment

Assessing the validity and reliability of the evaluation ensures its ability to accurately and consistently measure students’ learning outcomes. Validity examines the assessment questions that align with the learning objectives of the HTM course. The focus is mainly on nurses’ capacity to integrate technology into healthcare to reduce MEs, mortality, and morbidity rates. Reliability measures the test questions’ consistency over time, enabling students to achieve similar scores across repeated administrations (Li et al., 2021). The reliability of the assessment can be evaluated using the grading above criteria – Non-Performance, Basic, Proficient, and Distinguished. Adherence to the scoring guide indicates the reliability of the grading criteria (Authement & Dormire, 2020).

The strengths of the assessment include its reliability in comprehensively evaluating course learning outcomes. However, a weakness lies in the limited availability of simulation-based learning, which hinders students’ practical knowledge acquisition and ability to integrate technology effectively into healthcare services (Li et al., 2021).

References

Ali, K. A. G., Khalil, H. E. M., & El-Sharkawy, F. M. (2020). Impacts of online remote education on the learning process among nursing students. Open Journal of Nursing, 10(09), 810–830. https://doi.org/10.4236/ojn.2020.109057 

Authement, R. S., & Dormire, S. L. (2020). Introduction to the Online Nursing Education Best Practices Guide. SAGE Open Nursing, 6. https://doi.org/10.1177/2377960820937290 

De Lima, M., Stewart, K. M., Salas, R., Smetana, R., & Woodroof, M. (2022). Faculty collaboration in transitioning to NGN test item writing. Teaching and Learning in Nursing, 18(1), 188–192. https://doi.org/10.1016/j.teln.2022.11.001 

Farahani, B., Firouzi, F., & Chakrabarty, K. (2020). Healthcare iot. Intelligent Internet of Things, 515–545. https://doi.org/10.1007/978-3-030-30367-9_11 

Leaver, C. A., Stanley, J. M., & Veenema, T. G. (2021). Impact of the COVID-19 pandemic on the future of nursing education. Academic Medicine, Publish Ahead of Print(3), S82–S89. https://doi.org/10.1097/acm.0000000000004528 

Li, W., Gillies, R., He, M., Wu, C., Liu, S., Gong, Z., & Sun, H. (2021). Barriers and facilitators to online medical and nursing education during the COVID-19 pandemic: Perspectives from international students from low- and middle-income countries and their teaching staff. Human Resources for Health, 19(1). https://doi.org/10.1186/s12960-021-00609-9 

NURS FPX 6111 Assessment 2 Criteria and Rubric Development

Martinengo, L., Yeo, N. J. Y., Tang, Z. Q., Markandran, K. D., Kyaw, B. M., & Car, L. T. (2019). Digital education for the management of chronic wounds in health care professionals: Protocol for a systematic review by the digital health education collaboration. JMIR Research Protocols, 8(3), e12488. https://doi.org/10.2196/12488 

Martins, V. S. M., Santos, C. M. N. C., Bataglia, P. U. R., & Duarte, I. M. R. F. (2020). The teaching of ethics and the moral competence of medical and nursing students. Health Care Analysis, 29(2), 113–126. https://doi.org/10.1007/s10728-020-00401-1 

Miller, S. M., Hui-Lio, C., & Piliae, R. E. T. (2020). Health benefits of tai chi exercise. Nursing Clinics of North America, 55(4), 581–600. https://doi.org/10.1016/j.cnur.2020.07.002 

Ngiam, K. Y., & Khor, I. W. (2019). Big data and machine learning algorithms for health-care delivery. The Lancet Oncology, 20(5), e262–e273. https://doi.org/10.1016/s1470-2045(19)30149-4 

Pottle, J. (2019). Virtual reality and the transformation of medical education. Future Healthcare Journal, 6(3), 181–185. https://doi.org/10.7861/fhj.2019-0036 

Shah, M., Fuller, B., Gouveia, C., Mee, C. L., Baker, R. S., & San Pedro, M. O. Z. (2022). NCLEX-RN readiness: HESI exit exam validity and nursing program policies. Journal of Professional Nursing, 39, 131–138. https://doi.org/10.1016/j.profnurs.2022.01.010 

NURS FPX 6111 Assessment 1 Course Definition and Alignment Table

Name

Capella University

NURS-FPX 6111 Assessment and Evaluation in Nursing Education

Prof. Name

Date

Course Definition and Alignment Table

The selected course title is Healthcare Technology Management (HTM), aimed at equipping students with the skills to effectively utilize Information Technology (IT) resources in healthcare settings. This program focuses on integrating technology to optimize patient care delivery and improve health outcomes. With a duration of four months and totaling 18 credit hours, students enrolled in the BS Nursing program will receive lectures in classrooms equipped with computers for hands-on guidance. To ensure the highest standards of patient care, HTM will empower students to leverage IT to develop and manage innovative healthcare solutions.

Course Description Aligned with Educational Program Outcomes

The integration of  IT and medical expertise in HTM enables evidence-based techniques and practices in healthcare delivery. This discipline emphasizes quantitative analysis to assess complex information critically. HTM facilitates informed decision-making in diverse healthcare settings.

Vision

HTM entails studying and applying nursing data and expertise by integrating IT for efficient data management and utilization. HTM aims to improve patient outcomes while reducing healthcare expenditures (Robert, 2019). 

Rationale

In HTM, the foundation lies in utilizing statistics, to-the-point data, and far-reaching understanding. Just as nurses navigate information and knowledge to inform decision-making, HTM professionals leverage these elements to drive strategic advancements in healthcare technology (Umetsu, 2019). HTM students are encouraged to develop a realistic perspective of the healthcare landscape alongside strong technical skills. Emphasis is placed on gaining expertise in information system architecture, networking, and staying abreast of IT-driven innovations in healthcare (Umetsu, 2019). 

Assumptions:

• HTM integrates healthcare data and technology and assumes to improve patient outcomes.
• The quantitative aspects of HTM assume that it will enable thorough information analysis for well-informed decision-making. 
• The course assumes it will synchronize healthcare expertise with technological advancements to enhance patient care delivery and mitigate healthcare costs (Michaud, 2019).

Learning Objectives Aligned With Program Outcomes

Numerous learning objectives are implemented to empower HTM students and integrate technology with practical applications. Students will achieve the following.

• Master the use of healthcare information systems and technologies to optimize patient care delivery.
• Develop proficiency in implementing and managing medical devices and equipment in various healthcare settings.
• Analyze healthcare data to identify trends, patterns, and insights for informed decision-making in HTM.
• Demonstrate competency in utilizing telehealth and remote monitoring technologies for enhanced patient care and management.
• Stay abreast of emerging technologies and trends in HTM to adapt and respond to evolving healthcare needs effectively.

Program Description

The BSN program offers a comprehensive undergraduate curriculum to train proficient professionals in technology utilization. This program provides students with both theoretical understanding and practical competencies necessary for effective healthcare technology implementation. Emphasizing the integration of technological advancements in patient care it aligns seamlessly with the HTM course. This alignment ensures that students meet industry standards and are well-prepared to navigate the dynamic healthcare environment (Nazeha et al., 2020).

Program Outcomes

• After this program, students will be able to utilize healthcare technology for efficient patient care that aligns with the learning objective.
• Graduates will be able to utilize critical thinking in managing healthcare technology for effective decision-making.
• Following the learning objective, students will efficiently analyze healthcare data to identify trends and insights for informed decision-making in healthcare technology management.
• Students can effectively utilize telehealth and remote monitoring technologies to improve patient care and management.
• Students can stay updated with emerging technologies and trends in HTM to adapt to evolving healthcare needs aligned with the learning objective (Berga et al., 2020).

Evaluation of The Quality Alignment

Part 2 – Program Assessment and Evaluation Strategies Used in Nursing Course

Evaluation is fundamental to educational programs in HTM and the broader academic context. The success of an HTM program can be measured by its alignment with predetermined benchmarks regarding outcomes and performance monitoring, which are informed by industry and educational standards.

The HTM program spans four months and encompasses 18 credit hours of coursework. Students will focus on learning management techniques, utilizing technical equipment in healthcare settings, ensuring the quality and excellence of healthcare services, safeguarding patient data, and implementing strategies for disease prevention (Cox, 2019).

The strategies for evaluating the HTM program in healthcare include the following:

Integration of online education administration framework to complement traditional classroom instruction.

• Utilization of EHR during medical demonstrations for documenting observations.
• Assessment of nurses’ collaborative and communicative abilities alongside their assessment and grading skills to ensure effective teamwork and communication.
• Observational evaluation of nurses using various technologies to gather data for healthcare inspection procedures (Lee et al., 2020).
• Simulation-based assessments of nurses’ understanding and application of HTM principles in real-world healthcare settings, including proficiency in Information and Communication Technology (ICT) usage, integration of professional practices, and confidence in executing HTM tasks (Lee et al., 2020).

Various learning domains including cognitive, psychomotor, and affective would be assessed through several assessments to engage students and to promote their learning abilities.

Cognitive Domain

• Engages students in critical analysis and problem-solving as they analyze healthcare data and make informed decisions.
• Challenges students to apply evidence-based practices and make informed decisions in managing healthcare technologies.
• Another assessment requires students to analyze complex healthcare data sets, identifying trends and patterns for informed decision-making (Abbasi et al., 2023).

Psychomotor Domain

• Develops proficiency in implementing and managing medical devices and technologies, requiring hands-on skills and talent.

• This assessment involves physical assessments, administering treatments, or operating simulation equipment, directly engaging psychomotor skills.

Affective Domain

• This activity promotes empathy and professionalism when presenting findings and recommendations in a patient-centered manner.

• Encourages adaptability and responsiveness to emerging technologies and trends in healthcare technology management.

• These assessments foster empathy, communication, and professionalism when interacting with simulated patients or team members (Abbasi et al., 2023).

Missing Information and Knowledge Gaps

The field of HTM has witnessed a significant increase in the complexity of skills required by practitioners. This growth underscores the urgent need for a more precise portrayal of essential competencies within the HTM domain. Currently, there is a notable absence of universally accepted guidelines for conveying fundamental HTM skills in medical education curricula (Keskinocak & Savva, 2019). Additionally, the deficiency of studies exploring the essential informatics education for potential HTM educators has also been identified as a knowledge gap. HTM professionals must be evaluated not solely based on their performance in academic or practical assessments but also on their ability to demonstrate the aptitudes necessary for success in their professional endeavors (Keskinocak & Savva, 2019).

Aligning Relevant Professional Standards And Regulations

In a rapidly evolving healthcare field, expertise in HTM is essential for ensuring both quality and efficiency in healthcare and medical device departments. HTM focuses on optimizing healthcare professionals’ job duties by strategically using IT. The Healthcare Information and Management Systems Society (HIMSS) and the Association for the Advancement of Medical Instrumentation (AAMI) are prominent organizations comprising healthcare professionals dedicated to delivering superior patient care while maintaining financial responsibility (Douville, 2023).

There is a suggestion for HTM programs to prioritize intellectual over practical training to meet the growing demands of the healthcare industry. HTM professionals can benefit from pursuing a professional degree rather than a minimal or intermediate one to align with the expectations of the healthcare system (Douville, 2023). Degree-educated HTM professionals are better equipped to analyze complex scenarios, maintain composure under pressure, and make informed decisions based on evidence-based practices. This contrasts with individuals with vocational training who may not possess the same level of preparation (Douville, 2023).

References

Abbasi, M., Shirazi, M., Torkmandi, H., Homayoon, S., & Abdi, M. (2023). Impact of teaching, learning, and assessment of medical law on cognitive, affective and psychomotor skills of medical students: A systematic review. BMC Medical Education, 23(1). https://doi.org/10.1186/s12909-023-04695-2 

Berga, K.-A., Vadnais, E., Nelson, J., Johnston, S., Buro, K., Hu, R., & Olaiya, B. (2020). Blended learning versus face-to-face learning in an undergraduate nursing health assessment course: A quasi-experimental study. Nurse Education Today, 96, 104622. https://doi.org/10.1016/j.nedt.2020.104622 

Cox, C. A. (2019). Nurse manager job satisfaction and retention. Nursing Management (Springhouse), 50(7), 16–23. https://doi.org/10.1097/01.numa.0000558512.58455.68 

Douville, S. (2023). Advanced health technology. CRC Press. https://books.google.com.pk/books?id=0XmtEAAAQBAJ&printsec=frontcover&source=gbs_atb#v=onepage&q&f=false 

Keskinocak, P., & Savva, N. (2019). A review of the healthcare-management (modeling) literature published in manufacturing & service operations management. Manufacturing & Service Operations Management, 22(1), 1–222. https://doi.org/10.1287/msom.2019.0817 

NURS FPX 6111 Assessment 1 Course Definition and Alignment Table

Lee, R. L. T., West, S., Tang, A. C. Y., Cheng, H. Y., Chong, C. Y. Y., Chien, W. T., & Chan, S. W. C. (2020). A qualitative exploration of the experiences of school nurses during COVID-19 pandemic as the frontline primary health care professionals. Nursing Outlook, 69(3), 399–408. https://doi.org/10.1016/j.outlook.2020.12.003 

Michaud, S. (2019). Feature: As HTM evolves, soft skills become more important. Biomedical Instrumentation & Technology, 53(6), 438–442. https://doi.org/10.2345/0899-8205-53.6.438 

Nazeha, N., Pavagadhi, D., Kyaw, B. M., Car, J., Jimenez, G., & Tudor Car, L. (2020). A digitally competent health workforce: Scoping review of educational frameworks. Journal of Medical Internet Research, 22(11), e22706. https://doi.org/10.2196/22706 

Robert, N. (2019). How artificial intelligence is changing nursing. Nursing Management, 50(9), 30–39. https://doi.org/10.1097/01.numa.0000578988.56622.21 

Umetsu, K. (2019). Feature: An uncommon challenge: Leading an HTM department at a research institute. Biomedical Instrumentation & Technology, 53(5), 362–363. https://doi.org/10.2345/0899-8205-53.5.362 

Yao, R., Zhang, W., Evans, R., Cao, G., Rui, T., & Shen, L. (2021). Inequities in healthcare services caused by the adoption of digital health technologies: A scoping review. Journal of Medical Internet Research, 24(3), e34144. https://doi.org/10.2196/34144